High-Frequency (HF) radars measure the ocean surface currents at various spatial and temporal scales. These include tidal currents, wind-driven circulation, density-driven circulation and Stokes drift. Sequential assimilation methods updating the model state have been proven successful to correct the density-driven currents by assimilation of observations such as sea surface height, sea surface temperature and in-situ profiles. However, the situation is different for tides in coastal models since these are not generated within the domain, but are rather propagated inside the domain through the boundary conditions. For improving the modeled tidal variability it is therefore not sufficient to update the model state via data assimilation without updating the boundary conditions. The optimization of boundary conditions to match observations inside the domain is traditionally achieved through variational assimilation methods. In this work we present an ensemble smoother to improve the tidal boundary values so that the model represents more closely the observed currents. To create an ensemble of dynamically realistic boundary conditions, a cost function is formulated which is directly related to the probability of each boundary condition perturbation. This cost function ensures that the boundary condition perturbations are spatially smooth and that the structure of the perturbations satisfies approximately the harmonic linearized shallow water equations. Based on those perturbations an ensemble simulation is carried out using the full three-dimensional General Estuarine Ocean Model (GETM). Optimized boundary values are obtained by assimilating all observations using the covariances of the ensemble simulation. [less ▲]

High-Frequency (HF) radars measure the ocean currents at various spatial and temporal scales. These include tidal currents, wind-driven circulation, density-driven circulation and Stokes drift. Sequential assimilation methods updating the model state have been proven successful to correct the density-driven currents by assimilation of observations such as sea surface height, sea surface temperature and in-situ profiles. However, the situation is different for tides in coastal models since these are not generated within the domain, but are rather propagated inside the domain through the boundary conditions. For improving the modeled tidal variability it is therefore not sufficient to update the model state via data assimilation without updating the boundary conditions. The optimization of boundary conditions to match observations inside the domain is traditionally achieved through variational assimilation methods. In this work we present an ensemble smoother to improve the tidal boundary values so that the model represents more closely the observed currents. To create an ensemble of dynamically realistic boundary conditions, a cost function is formulated which is directly related to the probability of each perturbation. This cost function ensures that the perturbations are spatially smooth and that the structure of the perturbations satisfies approximately the harmonic linearized shallow water equations. Based on those perturbations an ensemble simulation is carried out using the full three-dimension General Estuarine Ocean Model (GETM). Optimized boundary values are obtained using all observations within the assimilation period using the covariances of the ensemble simulation. [less ▲]

An ensemble smoother scheme is presented to assimilate HF radar surface currents to improve tidal boundary conditions and wind forcings of a circulation model of the German Bight. To create an ensemble of ... [more ▼]

An ensemble smoother scheme is presented to assimilate HF radar surface currents to improve tidal boundary conditions and wind forcings of a circulation model of the German Bight. To create an ensemble of dynamically realistic tidal boundary conditions, a cost function is formulated which is directly related to the probability of each perturbation. This cost function ensures that the perturbations are spatially smooth and that the structure of the perturbations satisfies approximately the harmonic linearized shallow water equations. Based on those perturbations an ensemble simulation is carried out using the full three-dimensional General Estuarine Ocean Model (GETM). Optimized boundary values are obtained using all observations within the assimilation period using the covariances of the ensemble simulation. The approach acts like a smoother scheme since all observations are taken into account. Since the scheme aims to derive the optimal perturbation, it might be called Ensemble Perturbation Smoother. The final analysis is obtained by rerunning the model using the optimal perturbation to the boundary conditions. The analyzed model solution satisfies thus the model equations exactly and does not suffer from spurious adjustments often observed with sequential assimilation schemes. Model results are also compared to independent tide gage data. The assimilation did also reduce the model error compared to those sea level observations. The same scheme has also been used to correct surface winds. Surface winds are crucial for accurately modeling the marine circulation in coastal waters. The method is validated directly by comparing the analyzed wind speed to in situ measurements and indirectly by assessing the impact of the corrected winds on sea surface temperature (SST) relative to satellite SST. [less ▲]

The results of coastal ocean models depend critically on the accuracy of boundary and initial conditions and atmospheric forcing. The precision of coastal ocean models is limited among others by ... [more ▼]

The results of coastal ocean models depend critically on the accuracy of boundary and initial conditions and atmospheric forcing. The precision of coastal ocean models is limited among others by uncertainty in those forcing fields. Since high-frequency (HF) radar installations provide measurements over a relatively large area, the assimilation of these data has a high potential to reduce the errors in ocean models and to provide a dynamically consistent estimation of the ocean circulation. The assimilation of HF radar data is not without its own challenges: the spatial variation of the surface currents uncertainty, the high temporal resolution of HF radar data, the simultaneous presence of a wide range of processes with distinct spatial and temporal scales (tides and other surface gravity waves, mesoscale and wind-driven circulation), and the generally strong sensitivity of regional models to errors in the boundary conditions and atmospheric forcings. These processess are important aspects to consider in the application of data assimilation methods to HF radar measurements. The results of two data assimilation experiments on the West Florida Shelf (WFS) and the German Bight are presented. HF radar currents are assimilated in a nested West Florida Shelf based on an ensemble of model realizations with different wind forcings. The model is sequentially updated and a filter is implemented to reduce spurious surface-gravity waves. Results of the WFS model assimilating surface currents show an improvement of the model currents not only at the surface but also at depth compared to independent ADCP observations. This West Florida Shelf assimilation experiment does not include tides. Tides are not generated within the domain, but are rather propagated inside the domain through the boundary conditions. The potential of using HF radar data to reduce errors in tidal boundary conditions is shown in a model setup of the German Bight. For improving the modeled tidal variability it is not sufficient to update the model state without updating the boundary conditions. An ensemble smoother to improve the tidal boundary values is presented and validated with independent HF radar measurements and tide-gage data. The ensemble-scheme is also applied to improve the wind forcing by assimilation of surface currents. The improvement of the analyzed wind forcing is assessed by using in-situ wind measurements. [less ▲]

in Proceedings of Robust Methods for Power System State Estimation and Load Forecasting (2006)

In this paper we present a new tree-based ensemble method called “Extra-Trees”. This algorithm averages predictions of trees obtained by partitioning the inputspace with randomly generated splits, leading ... [more ▼]

In this paper we present a new tree-based ensemble method called “Extra-Trees”. This algorithm averages predictions of trees obtained by partitioning the inputspace with randomly generated splits, leading to significant improvements of precision, and various algorithmic advantages, in particular reduced computational complexity and scalability. We also discuss two generic applications of this algorithm, namely for time-series classification and for the automatic inference of near-optimal sequential decision policies from experimental data. [less ▲]

In this paper, we consider supervised learning under the assumption that the available memory is small compared to the dataset size. This general framework is relevant in the context of big data ... [more ▼]

In this paper, we consider supervised learning under the assumption that the available memory is small compared to the dataset size. This general framework is relevant in the context of big data, distributed databases and embedded systems. We investigate a very simple, yet effective, ensemble framework that builds each individual model of the ensemble from a random patch of data obtained by drawing random subsets of both instances and features from the whole dataset. We carry out an extensive and systematic evaluation of this method on 29 datasets, using decision tree-based estimators. With respect to popular ensemble methods, these experiments show that the proposed method provides on par performance in terms of accuracy while simultaneously lowering the memory needs, and attains significantly better performance when memory is severely constrained. [less ▲]

We introduce a new method for forecasting major El Niño/ La Niña events based on a wavelet mode decomposition. This methodology allows us to approximate the ENSO time series with a superposition of three ... [more ▼]

We introduce a new method for forecasting major El Niño/ La Niña events based on a wavelet mode decomposition. This methodology allows us to approximate the ENSO time series with a superposition of three periodic signals corresponding to periods of about 31, 43 and 61 months respectively with time-varying amplitudes. This pseudo-periodic approximation is then extrapolated to give forecasts. While this last one only resolves the large variations in the ENSO time series, three years hindcast as retroactive prediction allows to recover most of the El Niño/ La Niña events of the last 60 years. [less ▲]

in Publications de l'Association Internationale de Climatologie (1999), 11

For many years, shop location has been studied at different scales from a socio-economical point of view. Sunshine, on the other hand, is a local phenomenon well-known especially for climatologists and ... [more ▼]

For many years, shop location has been studied at different scales from a socio-economical point of view. Sunshine, on the other hand, is a local phenomenon well-known especially for climatologists and architects. This case-based contribution shows the relationships existing between sunshine and shop location on squares. [less ▲]